Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer system configured to provide a dynamic user interface relating to visualization of alerts of malicious network activity, the computer system comprising: an electronic data structure configured to store a plurality of clusters of data items, wherein each cluster of data items represents a group of related malicious network activities; and one or more hardware computer processors configured to execute code in order to cause the computer system to: access the electronic data structure including the plurality of clusters of data items; analyze the plurality of clusters of data items to determine, for each cluster of the plurality of clusters: a type of malicious network activity represented by the cluster, and a criticality of the malicious network activity represented by the cluster; further analyze the plurality of clusters of data items to determine respective numbers of clusters of the plurality of clusters having each of a plurality of types of malicious network activity; provide a dynamic user interface configured to display at least: a first visualization indicating, for each type of malicious network activity of the plurality of types of malicious network activity, respective portions of the plurality of clusters having the type of malicious network activity; and a second visualization indicating, for each cluster of the plurality of clusters, an alert corresponding to the cluster, wherein the alert visually indicates that criticality of the malicious network activity represented by the cluster; and automatically order the alerts indicated in the second visualization based on the respective determined criticalities of malicious network activity represented by the clusters corresponding to the alerts.
A computer system provides a dynamic user interface for visualizing alerts of malicious network activity. The system includes an electronic data structure storing clusters of data items, where each cluster represents a group of related malicious network activities. The system analyzes these clusters to determine the type and criticality of each malicious activity. It further analyzes the clusters to count how many belong to each type of malicious activity. The dynamic user interface displays two visualizations: the first shows the distribution of clusters by activity type, indicating the proportion of clusters for each type. The second visualization presents alerts for each cluster, with visual indicators showing the criticality of the associated malicious activity. The alerts are automatically ordered based on their criticality, allowing users to prioritize responses to the most severe threats. This system helps security analysts quickly assess and prioritize network threats by providing a clear, organized view of malicious activities and their severity.
2. The computer system of claim 1 , wherein the alert visually indicates that criticality of the malicious network activity represented by the cluster by at least one of: an icon, or a color.
This invention relates to cybersecurity systems that detect and analyze malicious network activity. The system identifies clusters of network events that exhibit similar malicious behavior, such as coordinated attacks or malware propagation. A key challenge is effectively communicating the severity or criticality of these detected threats to users, such as security analysts or administrators, in a clear and actionable manner. The system generates visual alerts for these clusters, where the alert includes a visual indicator that conveys the criticality of the malicious activity. The criticality is represented by at least one of an icon or a color. For example, a high-criticality cluster might be marked with a red icon or a red color, while a lower-criticality cluster could use a yellow or green indicator. This visual distinction helps users quickly prioritize responses based on threat severity. The system may also include additional features, such as clustering network events based on shared attributes like source IP addresses, timestamps, or attack patterns, and generating alerts that summarize the detected activity. The visual indicators ensure that users can rapidly assess the urgency of each alert without needing to analyze detailed data. This approach improves threat response efficiency by reducing cognitive load and enabling faster decision-making.
3. The computer system of claim 2 , wherein the second visualization further indicates, for each alert, the type of malicious network activity represented by the cluster corresponding to the alert.
This invention relates to computer systems for visualizing and analyzing malicious network activity. The system addresses the challenge of detecting and categorizing threats in network traffic by clustering related alerts and presenting them in a structured, interactive visualization. The system groups alerts into clusters based on shared characteristics, such as source, destination, or attack type, to reduce noise and highlight patterns. A first visualization displays these clusters, allowing users to explore relationships between them. A second visualization provides additional details for each alert, including the specific type of malicious activity associated with its cluster. This helps security analysts quickly identify and prioritize threats. The system may also include filtering tools to refine the view based on user-defined criteria, such as time, severity, or attack vector. By organizing alerts into clusters and providing clear visual indicators of threat types, the system improves threat detection efficiency and reduces response times.
4. The computer system of claim 1 , wherein the one or more hardware computer processors are further configured to execute code in order to cause the computer system to: access a plurality of cluster analysis rules; and for each cluster of the plurality of clusters: determine at least one of the plurality of cluster analysis rules that is associated with the type of malicious network activity represented by the cluster; analyze the cluster based on the at least one of the plurality of cluster analysis rules; and based on the analysis of the cluster, generate one or more human-readable conclusions regarding the cluster.
The invention relates to cybersecurity systems that analyze network traffic to detect and classify malicious activities. The problem addressed is the difficulty in automatically interpreting and summarizing clusters of network data that represent potential security threats, making it challenging for analysts to quickly understand and respond to detected anomalies. The system uses machine learning or statistical methods to group network traffic data into clusters, where each cluster represents a distinct pattern of malicious activity. To enhance interpretability, the system accesses a set of predefined cluster analysis rules, which are tailored to different types of cyber threats (e.g., malware infections, phishing campaigns, or denial-of-service attacks). For each cluster, the system identifies the relevant rules based on the type of malicious activity it represents. It then applies these rules to analyze the cluster, extracting meaningful insights. The analysis may involve statistical validation, behavioral pattern matching, or correlation with known threat indicators. Finally, the system generates human-readable conclusions, such as threat severity, attack vectors, or recommended mitigation steps, to assist security analysts in decision-making. This automation reduces the time and expertise required to assess and respond to cyber threats.
5. The computer system of claim 4 , wherein the second visualization further indicates, for each alert, at least one of the one or more human-readable conclusions regarding the cluster corresponding to the alert.
This invention relates to computer systems for visualizing and analyzing alerts generated by monitoring systems, particularly in environments where multiple alerts may be related or clustered. The problem addressed is the difficulty in interpreting and prioritizing alerts when they are presented in isolation, making it hard for users to understand the broader context or underlying patterns. The system improves alert visualization by grouping related alerts into clusters and providing human-readable conclusions about each cluster. These conclusions help users quickly grasp the significance of each cluster, such as identifying common causes or trends. The system generates a first visualization showing the clusters and a second visualization that further details each alert within a cluster, including at least one human-readable conclusion about the cluster. This allows users to efficiently assess the relevance and impact of alerts, reducing response time and improving decision-making. The invention is particularly useful in cybersecurity, network monitoring, or any domain where large volumes of alerts require rapid analysis.
6. The computer system of claim 4 , wherein the criticality of the malicious network activity represented by the cluster is determined based on a correlation between characteristics of the cluster and the at least one of the plurality of cluster analysis rules that is associated with type of malicious network activity represented by the cluster.
A computer system analyzes network traffic to detect and assess the criticality of malicious activities by clustering network events and applying predefined rules. The system groups related network events into clusters based on shared characteristics, such as source IP addresses, destination ports, or payload patterns. Each cluster is then evaluated against a set of cluster analysis rules, which are predefined criteria that define different types of malicious activities, such as distributed denial-of-service (DDoS) attacks, data exfiltration, or unauthorized access attempts. The system determines the criticality of the detected malicious activity by correlating the cluster's characteristics with the specific rule that matches it. For example, if a cluster matches a rule for a high-severity attack, the system assigns a higher criticality score. The system may also adjust criticality based on additional factors, such as the frequency of events in the cluster or the sensitivity of the targeted network resources. This approach enables automated prioritization of security threats, allowing administrators to focus on the most severe risks first. The system may integrate with existing security tools to provide real-time alerts or trigger automated mitigation actions.
7. The computer system of claim 6 , wherein the degree of correlation is based on both an assessment of risk associated with the cluster and a confidence level in accuracy of the assessment of risk.
The invention relates to a computer system for analyzing and assessing risk in data clusters, particularly in contexts where data is grouped into clusters for analysis. The system addresses the challenge of accurately evaluating risk within these clusters, where traditional methods may fail to account for both the inherent uncertainty in risk assessment and the reliability of the assessment itself. The system determines a degree of correlation between data points within a cluster by considering two key factors: the assessed risk level of the cluster and the confidence level in the accuracy of that risk assessment. By integrating these factors, the system provides a more nuanced and reliable evaluation of risk, improving decision-making processes that rely on cluster-based data analysis. The system may also include mechanisms for dynamically adjusting the correlation degree based on changes in risk factors or confidence levels, ensuring continuous refinement of risk assessments. This approach is particularly useful in fields such as cybersecurity, financial risk management, and fraud detection, where accurate risk assessment is critical. The invention enhances the robustness of risk analysis by incorporating both the magnitude of risk and the certainty of the assessment, leading to more informed and reliable outcomes.
8. The computer system of claim 4 , wherein the criticality is represented by a score.
This invention relates to computer systems for assessing and managing criticality in software or hardware components. The system evaluates the importance or criticality of components within a computing environment, such as software modules, hardware devices, or network elements, to prioritize maintenance, security, or resource allocation. The criticality is quantified using a numerical score, which allows for objective comparison and decision-making. The system may analyze factors like component dependencies, failure impact, usage frequency, or security vulnerabilities to generate this score. By assigning a criticality score, the system enables automated or manual prioritization of components for updates, patches, or resource allocation, improving system reliability and efficiency. The invention may integrate with monitoring tools, configuration management systems, or security frameworks to dynamically adjust criticality scores based on real-time data. This approach helps organizations optimize maintenance efforts and reduce risks associated with high-criticality components.
9. The computer system of claim 8 , wherein a relatively higher score indicates a cluster that is relatively more important for a human analyst to evaluate, and a relatively lower score indicates a cluster that is relatively less important for the human analyst to evaluate.
This invention relates to a computer system for analyzing and prioritizing clusters of data, particularly in the context of human analyst evaluation. The system addresses the challenge of efficiently identifying which clusters of data are most relevant or important for further review by a human analyst, thereby optimizing the analyst's time and resources. The computer system processes data to generate clusters, which are groups of related data points. Each cluster is assigned a score that reflects its importance or relevance for human evaluation. A higher score indicates that the cluster is more important and should be prioritized for review, while a lower score indicates that the cluster is less important and may be deprioritized. The scoring mechanism helps automate the prioritization process, ensuring that analysts focus on the most critical clusters first. The system may also include additional features, such as generating a visual representation of the clusters, allowing analysts to interact with the data, and providing tools for further analysis. The scoring and prioritization process can be based on various factors, including the size of the cluster, the frequency of certain data points within the cluster, or other relevance metrics. By automating the prioritization of clusters, the system enhances efficiency and reduces the cognitive load on human analysts, enabling more effective data analysis.
10. The computer system of claim 8 , wherein the score is selected from high, medium, or low.
A computer system is designed to evaluate and categorize data or processes based on a scoring mechanism. The system assigns a score to an input, where the score is selected from three predefined categories: high, medium, or low. This scoring system helps in prioritizing, filtering, or decision-making based on the evaluated input. The system may process various types of data, such as user inputs, system performance metrics, or other measurable parameters, and applies a predefined scoring algorithm to determine the appropriate category. The scoring categories (high, medium, low) provide a simplified, standardized way to assess and compare different inputs or processes. This system is useful in applications where quick, standardized evaluations are needed, such as risk assessment, performance monitoring, or decision support systems. The scoring mechanism ensures consistency and clarity in evaluations, allowing users or other systems to easily interpret and act on the results. The system may integrate with other components to further process or utilize the scored data, such as triggering actions based on the assigned category.
11. The computer system of claim 4 , wherein generating the one or more human-readable conclusions is further based on one or more conclusion templates that are populated with data associated with the cluster.
This invention relates to a computer system for analyzing data and generating human-readable conclusions. The system addresses the challenge of extracting meaningful insights from large datasets by clustering related data points and generating structured, interpretable outputs. The system first processes input data to identify patterns or relationships, grouping similar data into clusters. Each cluster is then analyzed to extract relevant features or metrics. To produce human-readable conclusions, the system uses predefined templates that are dynamically populated with the extracted data. These templates ensure consistency and clarity in the generated outputs, making complex data analysis results accessible to non-technical users. The system may also incorporate additional context or domain-specific knowledge to refine the conclusions. By automating the interpretation of clustered data, the invention streamlines decision-making processes in fields such as business analytics, healthcare, and scientific research. The use of templates ensures that conclusions are standardized and easily understandable, reducing the need for manual interpretation. The system may further adapt the templates based on user feedback or evolving data patterns to improve accuracy over time.
12. The computer system of claim 4 , wherein the one or more human-readable conclusions each comprise a phrase or sentence including one or more indications of summary or aggregated data associated with a plurality of the data items of the cluster.
The invention relates to a computer system for analyzing and summarizing data clusters to generate human-readable conclusions. The system addresses the challenge of extracting meaningful insights from large datasets by automatically identifying patterns and relationships within clustered data, then presenting them in a concise, interpretable format. The system processes a dataset to group related data items into clusters based on shared characteristics or behaviors. For each cluster, the system analyzes the grouped data items to identify key trends, statistics, or other aggregated metrics. These insights are then synthesized into human-readable phrases or sentences, which may include summary statistics, aggregated values, or other indicators derived from the cluster's data items. The conclusions are designed to be easily understood by users without requiring deep technical expertise. The system may also support interactive exploration, allowing users to refine or adjust the clustering and summary generation process based on their specific needs. This approach enhances data interpretability and decision-making by transforming complex datasets into actionable insights.
13. The computer system of claim 1 , wherein the one or more hardware computer processors are further configured to execute code in order to cause the computer system to: receive, via the first visualization of the dynamic user interface, a user selection of a first type of malicious network activity from the plurality of types of malicious network activity; and automatically update at least the second visualization of the dynamic user interface to filter the alerts to only those alerts corresponding to clusters associated with the selected first type of malicious network activity.
A computer system monitors and analyzes network traffic to detect and visualize malicious activities. The system processes network data to identify patterns and clusters of suspicious behavior, generating alerts for potential threats. A dynamic user interface displays visualizations of these alerts, allowing users to interact with the data. The interface includes multiple visualizations, such as graphs or charts, that represent different aspects of the network activity, including the types, sources, and severity of detected threats. The system enhances user interaction by enabling dynamic filtering of alerts based on user selections. When a user selects a specific type of malicious activity from a predefined list, the system automatically updates the visualizations to display only alerts related to that selected activity. This filtering helps users focus on relevant threats, improving efficiency in threat detection and response. The system dynamically adjusts the visualizations in real-time, ensuring that the displayed data remains accurate and up-to-date as new alerts are generated or existing ones are modified. This interactive approach allows security analysts to quickly identify and investigate specific types of malicious behavior within the network.
14. A computer-implemented method comprising: by one or more hardware computer processors executing code: communicating with an electronic data structure configured to store a plurality of clusters of data items, wherein each cluster of data items represents a group of related malicious network activities; accessing the electronic data structure including the plurality of clusters of data items; analyzing the plurality of clusters of data items to determine, for each cluster of the plurality of clusters: a type of malicious network activity represented by the cluster, and a criticality of the malicious network activity represented by the cluster; further analyzing the plurality of clusters of data items to determine respective numbers of clusters of the plurality of clusters having each of a plurality of types of malicious network activity; providing a dynamic user interface configured to display at least: a first visualization indicating, for each type of malicious network activity of the plurality of types of malicious network activity, respective portions of the plurality of clusters having the type of malicious network activity; and a second visualization indicating, for each cluster of the plurality of clusters, an alert corresponding to the cluster, wherein the alert visually indicates that criticality of the malicious network activity represented by the cluster; and automatically ordering the alerts indicated in the second visualization based on the respective determined criticalities of malicious network activity represented by the clusters corresponding to the alerts.
This invention relates to cybersecurity, specifically to systems for analyzing and visualizing malicious network activities. The problem addressed is the difficulty in efficiently identifying, categorizing, and prioritizing threats in large datasets of network security events. The solution involves a computer-implemented method that processes clusters of data items representing groups of related malicious activities. The system accesses an electronic data structure storing these clusters and analyzes them to determine the type and criticality of each cluster's malicious activity. It further quantifies the prevalence of different activity types across the clusters. A dynamic user interface displays two visualizations: one showing the distribution of activity types among the clusters and another showing alerts for each cluster, with visual indicators of criticality. The alerts are automatically ordered by criticality to prioritize high-risk threats. This approach enhances threat detection and response by providing an organized, prioritized view of malicious activities, enabling security analysts to focus on the most critical threats first.
15. The computer-implemented method of claim 14 , wherein the alert visually indicates that criticality of the malicious network activity represented by the cluster by at least one of: an icon, or a color.
This invention relates to cybersecurity systems that detect and analyze malicious network activity. The problem addressed is the difficulty in effectively communicating the severity or criticality of detected threats to users, particularly in environments where multiple threats are clustered together. Existing systems often fail to provide clear visual indicators that quickly convey the urgency or impact of a detected threat, leading to delayed or ineffective responses. The invention describes a computer-implemented method for enhancing threat visualization in network security monitoring. It involves clustering detected malicious network activities into groups based on their characteristics, such as source, destination, or behavior patterns. Once clustered, the system generates an alert that visually represents the criticality of the malicious activity within each cluster. The visual indication may include an icon or a color-coded representation, allowing security personnel to quickly assess the severity of the threat. For example, a red icon or color may indicate a high-criticality threat, while a yellow or green icon may represent lower severity threats. This visual distinction helps prioritize responses and improves decision-making in real-time security operations. The method ensures that critical threats are immediately identifiable, reducing response times and minimizing potential damage.
16. The computer-implemented method of claim 15 , wherein the second visualization further indicates, for each alert, the type of malicious network activity represented by the cluster corresponding to the alert.
This invention relates to cybersecurity and network monitoring, specifically to methods for visualizing and analyzing malicious network activity. The problem addressed is the difficulty in identifying and interpreting patterns of malicious behavior in large-scale network data, which often results in overwhelming or unclear alert data for security analysts. The method involves generating visualizations of network activity data to help analysts detect and investigate potential threats. A first visualization displays clusters of network activity, where each cluster represents a group of related events or behaviors that may indicate malicious activity. A second visualization provides additional details about these clusters, including the specific type of malicious activity they represent. This allows analysts to quickly understand the nature of detected threats and prioritize their response. The method also includes filtering and grouping network activity data based on predefined criteria, such as time, source, or destination, to reduce noise and focus on relevant patterns. By correlating multiple alerts and presenting them in a structured, visual format, the invention improves the efficiency and accuracy of threat detection and response. The system dynamically updates the visualizations as new data is processed, ensuring analysts have the most current view of network activity. This approach enhances situational awareness and reduces the cognitive load on security teams.
17. The computer-implemented method of claim 14 further comprising: by the one or more hardware computer processors executing code: accessing a plurality of cluster analysis rules; and for each cluster of the plurality of clusters: determining at least one of the plurality of cluster analysis rules that is associated with the type of malicious network activity represented by the cluster; analyzing the cluster based on the at least one of the plurality of cluster analysis rules; and based on the analysis of the cluster, generating one or more human-readable conclusions regarding the cluster.
This invention relates to cybersecurity and network monitoring, specifically improving the analysis of detected malicious network activity. The problem addressed is the difficulty in efficiently and accurately interpreting large volumes of network traffic data to identify and understand malicious behavior patterns. Traditional methods often rely on manual analysis or basic clustering techniques, which can be time-consuming and may miss subtle indicators of threats. The method involves using one or more hardware computer processors to perform automated cluster analysis of network activity data. The system first groups network activity into clusters based on shared characteristics, such as source IP addresses, destination ports, or payload patterns. Each cluster represents a potential instance of malicious activity, such as a botnet, DDoS attack, or data exfiltration. To enhance analysis, the system accesses a predefined set of cluster analysis rules. These rules are tailored to different types of malicious activity, such as detecting command-and-control communications or identifying brute-force attacks. For each cluster, the system determines which rules apply based on the type of activity it represents. The cluster is then analyzed according to these rules, which may involve statistical analysis, pattern matching, or behavioral modeling. The results of this analysis are translated into human-readable conclusions, such as threat severity, attack vectors, or recommended mitigation steps. This automation reduces the cognitive load on security analysts and improves response times by providing actionable insights directly from the clustered data. The method ensures that even complex or novel attack patterns are systematically evaluated, enhancing overall network security.
18. The computer-implemented method of claim 17 , wherein the criticality of the malicious network activity represented by the cluster is determined based on a correlation between characteristics of the cluster and the at least one of the plurality of cluster analysis rules that is associated with type of malicious network activity represented by the cluster.
This technical summary describes a method for assessing the criticality of malicious network activity detected in a computer network. The method involves analyzing clusters of network activity to identify patterns indicative of malicious behavior, such as cyberattacks or unauthorized access. Each cluster is evaluated based on predefined cluster analysis rules, which define characteristics associated with specific types of malicious activity. The criticality of the detected activity is determined by correlating the observed characteristics of the cluster with the relevant rules. For example, if a cluster exhibits traits matching a rule for a high-severity attack, such as rapid data exfiltration or repeated unauthorized login attempts, the system assigns a higher criticality level. This approach enables automated prioritization of threats based on their potential impact, allowing security teams to focus on the most severe risks first. The method improves threat detection accuracy by leveraging rule-based analysis of clustered network behavior, reducing false positives and enhancing response efficiency.
19. The computer-implemented method of claim 18 , wherein the degree of correlation is based on both an assessment of risk associated with the cluster and a confidence level in accuracy of the assessment of risk.
The invention relates to a computer-implemented method for evaluating clusters of data, particularly in the context of risk assessment. The method addresses the challenge of accurately determining the risk associated with a cluster of data points, where the assessment must account for both the inherent risk of the cluster and the confidence in the accuracy of that risk assessment. The method involves analyzing a cluster of data to compute a degree of correlation between the cluster and a predefined risk model. This correlation is determined by evaluating two key factors: the assessed risk level of the cluster and the confidence level in the accuracy of that risk assessment. The assessed risk level quantifies the potential risk posed by the cluster, while the confidence level reflects the reliability of the risk assessment itself. By combining these factors, the method provides a more robust and nuanced evaluation of the cluster's risk profile. This approach is particularly useful in applications where risk assessment must be both precise and reliable, such as financial fraud detection, cybersecurity threat analysis, or healthcare diagnostics. The method ensures that high-risk clusters are identified with greater certainty, while also accounting for uncertainties in the assessment process. The degree of correlation serves as a metric that can be used to prioritize further investigation or mitigation actions based on both risk severity and assessment confidence.
20. The computer-implemented method of claim 14 further comprising: by the one or more hardware computer processors executing code: receiving, via the first visualization of the dynamic user interface, a user selection of a first type of malicious network activity from the plurality of types of malicious network activity; and automatically updating at least the second visualization of the dynamic user interface to filter the alerts to only those alerts corresponding to clusters associated with the selected first type of malicious network activity.
This invention relates to cybersecurity systems that analyze and visualize network traffic to detect and mitigate malicious activities. The problem addressed is the difficulty in efficiently identifying and investigating specific types of malicious network behavior among large volumes of security alerts. Existing systems often overwhelm users with unfiltered data, making it challenging to focus on relevant threats. The method involves a dynamic user interface that displays multiple visualizations of network alerts. A first visualization presents a plurality of types of malicious network activity, such as phishing, malware, or unauthorized access. A second visualization clusters alerts based on their characteristics, such as source, destination, or behavior patterns. When a user selects a specific type of malicious activity from the first visualization, the system automatically filters the second visualization to display only alerts associated with clusters linked to the selected activity type. This filtering helps users quickly narrow down relevant threats without manual sorting, improving efficiency in threat detection and response. The system dynamically updates the visualizations in real-time as new alerts are received, ensuring continuous monitoring of network security. The method enhances situational awareness by correlating alerts with specific malicious behaviors, reducing false positives, and enabling faster incident response.
Unknown
October 6, 2020
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